Curriculum learning is a useful method for learning sparse reward functions. Many problems like robots navigation have sparse reward function because the search space is extremely large and only goal state produces a positive reward. Curriculum learning gradually increases the difficulty of the problem by starting from the states close to the goal state and progressively increase the distance. A curriculum learning method inspired by dynamic programming methods is introduced in [1]} for goal oriented tasks like robot navigation. The motivation is to handle the sparsity of the reward function because the reward value is zero for all the moves except the one that takes the agent to the goal state. Based on this work, the robot starts to learn by searching from states that are close to the goal. The process is continued until showing mastery on the current setting. Then, new start states are added to the search space by a random walk from previous start states. This so called reverse learning is helpful to solve the problem of sparsity.
